On Improving the accuracy with Auto-Encoder on Conjunctivitis

Applying the classification approach in machine learning to medical field is a promising direction as it could potentially save a large amount of medical resources and reduce the impact of error-prone subjective diagnosis. However, low accuracy is currently the biggest challenge for classification. So far many approaches have been developed to improve the classification performance and most of them are focusing on how to extend the layers or the nodes in the Neural Network (NN), or combining a classifier with the domain knowledge of the medical field. These extensions may improve the classification performance. However, these classifiers trained on one datasets may not be able to adapt to another dataset. Meanwhile, the layers and the nodes of the neural network cannot be extended infinitely in practice. To overcome these problems, in this paper, we propose an innovative approach which is to employ the Auto-Encoder (AE) model to improve the classification performance. Specifically, we make the best of the compression capability of the Encoder to generate the latent compressed vector which can be used to represent the original samples. Then, we use a regular classifier to perform classification on those compressed vectors instead of the original data. In addition, we explore the classification performance on different extracted features by enumerating the number of hidden nodes which are used to save the extracted features. Comprehensive experiments are conducted to validate our proposed approach with the medical dataset of conjunctivitis and the STL-10 dataset. The results show that our proposed AE-based model can not only improve the classification accuracy but also be beneficial to solve the problem of False Positive Rate. (C) 2019 Elsevier B.V. All rights reserved.